Three Indexes Estimation in Extracting Change Area
from Remote Sensing Image by Fuzzy Theory and
Back Propagation Network (BPN)
Ting-Shiuan Wang, Teng-To Yu, Pei-Ling Li, Wei-Ling Lin
Department of Resource Engineering, National Cheng-Kung University, Tainan, Taiwan
n4896111@mail.ncku.edu.tw; yutt@mail.ncku.edu.tw
Abstract- This study presents the technology to combine the
remote sensing image of SPOT and FORMOSAT-2 satellite
image by Fuzzy theory and Back Propagation Network (BPN).
This method adopt three experience identify factors of NDVI,
shape, and color to establish the membership function. The fuzzy
results show that the successful rate of identification was about
87 percent. The BPN results show that the successful rate of
identification was about 91 percent. Therefore the result proves
that the three indexes are the best choice in extracting of change
area from image, because the error is the lowest among the two
methods in this study.
Keywords- Component; Fuzzy Theory; Image Extraction;
Image Variation; Image Identification; Back Propagation Network
(BPN)

I. INTRODUCTION
The objective of monitoring the change of catchment area
is to avoid damage from nature disaster or excessive
development. Previous method uses software to identify the
build land category, followed by human experience to check it
one by one. However, it needs a lot of time to identify image
from remote sensing images. If it is possible to automatically
study and train the capability of selection system, then the
time of distinguish can be effectively reduced.
For example, Meng-Lung Lin and Cheng-Wu Chen used
fuzzy model and remote sensing data about Normalized
Difference Vegetation Index (NDVI) in the winter, spring,
summer, and fall of 2000 and 2005. Their research analyzes
land cover maps and landscape sensitivity, and then estimate
the sensitivity of the ecosystems in the landscape [1]. Natalie
Campos etc., 2011 used High-resolution imagery and find it
were successfully assess map changes in land-cover patterns in
research area. However, previous these researches in similar
area were comparing lacking. They used NDVI image
differencing and Classification Tree (CT) methods in their
study. Then analyze to connect time with the changes in
spatial distribution of mountain resort development (MRD)[2].
Using image segmentation software to produce image
segmentation vector and extract the variation region is a
common practice for remote sensing technique in the past. The
process is: selecting image to segment, outputting the
segmentation vector file, using the software to extract the
vector layer, and screening for variation area. The work is
done by the accumulation of human experience. The memory
capability of human can accumulate the previous experience,
and it can compile the things touched to the rules of
information.
Human brain can trigger the corresponding rules to the
next move. If there were no corresponding rules, it can also
trigger the fuzzy space for self-learning and reasoning. This

capability can be simulated by the case base reasoning with
fuzzy theory. Therefore, we use the fuzzy theory and case base
reasoning to construct a quantitative fuzzy space structure to
learn the human brain logic and estimate the variation region
in the remote sensing image.
II. RESEARCH AREA
The research area locates in Shihmen Reservoir in
Taoyuan County (Fig. 1). Base on the report (Level 3
Management of Shihmen Reservoir) from Water Resources
Agency, Ministry of Economic Affairs, it can be seen that the
areas of orchard expand rapidly and mainly spread on sloping
fields at altitude of 800 meters which is good for planting
temperate fruit trees (peaches) but lower altitude areas are for
tangerines. The usage of lands follows by the demand of
tourism and economy benefits. It has been a significant change
compared with earlier types of crops; therefore it is essential to
monitor the environment of Shihmen Reservoir.

Fig. 1 Shihmen Reservoir

If telemetry can be used to monitor this area, it can quickly
browse the full view of reservoir lands and strengthen the
ability of monitoring variation region instead of spending great
amount of time and money by using manpower. It can
improve the traditional way of monitoring to gain efficiency.
What’s more, there have drafted down some preventive
measures of man-made or nature disaster to reduce the
influence from disaster and ensure lands can last forever.
III. METHODOLOGY
Three indexes were adopted in this study as experience
rules for quick discrimination, they were: differences in NDVI
values, shape ratio, and color difference values (Table І and
Figs. 2-4).

TABLE ⅠMEMBERSHIP FUNCTION AND NUMERICAL VALUE OF THE THREE
EVALUATION INDEXES

Evaluation
Index of
Variation
Regional

Differences
in NDVI
Values

Type if
Membership
Function

Trapezoidal

Attribute if
Fuzzy
Interval

Membership
Function
Value

NDVI
Differences
Values is
Positive

0.3～2

NDVI No
Change

-0.3～0.3

NDVI
Differences
Values is
Negative

-2～-0.3

No Change

1 To 1.5
Times

Diffusion
Change

1.5 Times To
10 Times

Reduction
Change

0～1 Time

Similar

-30～30

Dissimilar,
Positive
Value

30～765

Dissimilar,
Negative
Value

-765～-30

Fig. 4 Color difference values
Shape Ratio

Color
Difference
Value

Trapezoidal

Trapezoidal

A. Differences in NDVI Values
Chao-Yuan Lin etc., 2010 used multi-temporal satellite
images index (NDVI) to automatically extract landslide and
image classification. Usually, image classification uses NDVI
to increase accuracy. Because it can effective to classify the
bare land and vegetation cover [3].The α value, NDVI [4], is
to use the characteristic of green plant which can absorb the
red light and reflect the near red wave. According to wave
length from the remote sensing data, it can provide the plant
growth trend for the variation area. The meaning of NDVI
value difference is to compare the ratio of red light absorption
and near-infrared light reflection by green plant in the remote
sensing image. The numerical value of NDVI difference is the
difference of the early image and late image. Its value was
ranged between -2～2.
1)
Define the NDVI value of previous image is a, and
the value of post-image is b. The range of a single NDVI value
is between -1~1.
2)
Let f(a) =｛1,-1｝f(b)=｛-1,1｝,then f(b)-f(a) = -2
and 2 , so the maximum threshold value is between -2~2.
3)
The different value of NDVI is in the range between
-2~2. According to the induction of manual experience, if the
difference value of NDVI is lower than 0.3, it could be
regarded as having no change.

Fig. 2 Differences in NDVI values

4)
Let f(a)= ｛0.2,0.5 ｝f(b)= ｛0.5,0.2 ｝, we set the
range as if the value is between -0.3 ~ 0.3, then there is no
change. If the value is between 0.2~0.3, then there is change.
The function of NDVI is to rapidly analyze the plant
growth rate in the variation region. Otsu has proposed a rule
for plant in the remote sensing image in 1979[5].The color
value of image was divided into two areas as vegetation and
non-vegetation.

NDVI 

NIR - R 
NIR  R 

(1)
Where, NIR is the near infrared band and R is the red band.
B. Shape Ratio
The shape ratio (α β) is the area after the event divided by
the area before the event. The outer shape is used to analyze
whether it has achieved the variation, as shown in Fig. 5.
Fig. 3 Shape ratio
C World Academic Publishing
IJRSA Vol.2 Iss. 2 2012 PP.30-35 www.ijrsa.org ○
-31-

International Journal of Remote Sensing Applications

IJRSA
0
xa
ba
f x ,a ,b ,c ,d   { 1
dx
d c
0

xa
a xb
bxc
cxd
xd

a  b ,c  d
The trapezoidal function was chosen in this study because
the interval of threshold value for the three indexes in this
study is relatively fixed. Twenty seven rules of < IF…THEN >
were formulated as the following:
1)
If (NDVI is no change) and (shape is no change) and
(color is Similar) then (expected value is no change)

Fig. 5 Shape ratio

The definition of shape ratio is the ratio of post stage area
to the previous stage area. It has to be assigned with the value
according to the error factors of location, shape, and twist, etc.
If the value is lower than 1, it is the reduction change. If the
value is 1 to 1.5, then there is no change. If the value is greater
than 1.5, then it is the diffusion change.
The shape ratio is not applicable to the land use type as:
from never to have, from having to no, and the simplest
change of type. The shape ratio is applicable to the condition
when the land use types have no change, and only the area
shape changes. The shape ratio is also applicable to the area
which has been labeled as the variable region.

   
Shape 
 

(2)
C. Color Difference Values
The color difference is the color before and after the event.
In any pixel of the remote sensing data, the maximum value of
RGB will not exceed 255+255+255 = 765, as shown in Fig. 6.

2)
If (NDVI is no change) and (shape is no change) and
(color is Dissimilar) then (expected value is yes change)
3)
If (NDVI is no change) and (shape is no change) and
(color is Dissimilar) then (expected value is yes change)
4)
If (NDVI is no change) and (shape is Reduction) and
(color is Similar) then (expected value is yes change)
5)
If (NDVI is no change) and (shape is Reduction) and
(color is Dissimilar) then (expected value is yes change)
6)
If (NDVI is no change) and (shape is Reduction) and
(color is Dissimilar) then (expected value is yes change)
7)
If (NDVI is no change) and (shape is Diffusion) and
(color is Similar) then (expected value is yes change)
8)
If (NDVI is no change) and (shape is Diffusion) and
(color is Dissimilar) then (expected value is yes change)
9)
If (NDVI is no change) and (shape is Diffusion) and
(color is Dissimilar) then (expected value is yes change)
10) If (NDVI is negative) and (shape is no change) and
(color is Similar) then (expected value is yes change)
11) If (NDVI is negative) and (shape is no change) and
(color is Dissimilar) then (expected value is yes change)
12) If (NDVI is negative) and (shape is no change) and
(color is Dissimilar) then (expected value is yes change)
13) If (NDVI is negative) and (shape is Reduction) and
(color is Similar) then (expected value is yes change)
14) If (NDVI is negative) and (shape is Reduction) and
(color is Dissimilar) then (expected value is yes change)
15) If (NDVI is negative) and (shape is Reduction) and
(color is Dissimilar) then (expected value is yes change)
16) If (NDVI is negative) and (shape is Diffusion) and
(color is Similar) then (expected value is yes change)

Fig. 6 Color difference values

IV. CONSTRUCT FUZZY
The MATLAB fuzzy toolbox was used to construct a
structure of rapid extraction. Three groups of indicators were
identified. The functional forms and membership function
value were shown in Figs. 4~7. Since every index has three
attribute of fuzzy interval. So every index has four critical
ranges. Kaufmann and Gupta [6] induced several types of
membership function. Most suitable for all indexes is the
trapezoidal fuzzy number defined as:

17) If (NDVI is negative) and (shape is Diffusion) and
(color is Dissimilar) then (expected value is yes change)
18) If (NDVI is negative) and (shape is Diffusion) and
(color is Dissimilar) then (expected value is yes change)
19) If (NDVI is positive) and (shape is no change) and
(color is Similar) then (expected value is yes change)
20) If (NDVI is positive) and (shape is no change) and
(color is Dissimilar) then (expected value is yes change)

21) If (NDVI is positive) and (shape is no change) and
(color is Dissimilar) then (expected value is yes change)
22) If (NDVI is positive) and (shape is Reduction) and
(color is Similar) then (expected value is yes change)
23) If (NDVI is positive) and (shape is Reduction) and
(color is Dissimilar) then (expected value is yes change)
24) If (NDVI is positive) and (shape is Reduction) and
(color is Dissimilar) then (expected value is yes change)
25) If (NDVI is positive) and (shape is Diffusion) and
(color is Similar) then (expected value is yes change)
26) If (NDVI is positive) and (shape is Diffusion) and
(color is Dissimilar) then (expected value is yes change)
27) If (NDVI is positive) and (shape is Diffusion) and
(color is Dissimilar) then (expected value is yes change)
The fourth stage investigation data from the project of
SWCB-97-157 in 2008 were taken as the experience cases.
Ho-Wen Chen etc., 2009 developed a neural-fuzzy inference
approach with used the FORMOSAT-2 (8m spatial resolution)
multi-spectral satellite image. It can to analysis the land use
and land cover patterns in a fast growing city. Their each
fuzzy-neural rule incorporated with both the satellite image
spectral and textural features. So, it increased the advantages
of classification efficiency and accuracy [7].
However, in this paper try to use better high resolution
satellite fusion images (FORMOSAT-2 2m spatial resolution
and SPOT) with three indexes, the results were further verified.
The expected value of variations was calculated by the module
and obtains the value of 0.67. The module chart is shown in
Fig. 7. It means that if there is a possibility of change, its value
will be greater than 0.67. Otherwise, the value of change will
be lower than 0.67. The results of experience cases were
shown in Table ІІ, Ⅲ. From the table results, there are three
cases with the expected value of variance lower than 0.67
among the 32 cases. So the success rate of distinguish is (557)/55=0.87. The recognition rate is about 87%.

V. NEURAL NETWORK MODEL
One neural network model, the Back Propagation Network
(BPN) was used in this study to find the best results [8]. The
experimentation data of BPN are to the real value (Table Ⅳ).

A. Training and Test Model
In this mode, the original data was used to establish the
model. The input was the unit for training, the number of
hidden layer, learning cycle, and speed of learning cycle, etc.
B. Authentication Model
The authentication mode is to use the non-training data to
evaluate the reliability of the model. The results can be the
information of the scattering plot, matrix, or error analysis.

IJRSA
The output variable will also be estimated to compare their
error.
C. Inference Model
The inference model is used to predict certain condition
that we want to know. The experienced value was derived
from
the
trained
mode.
(citation :
http://www.wisdomsoft.com.tw)
The value of assessment index is calculated as the
following:
assessment index 

Difference s in NDVI values   100 

4
( Shape ratio)
(Color difference value)
 100 
 100
15
1530

(3)

The prediction results of these BPN neural network
schemes were shown in (Table IV).The error of BPN to the
real value is 91.04%.
VI. CONCLUSION
It is necessary to appropriately monitor the environment
according to the characteristic of land use in the watershed.
Remote sensing technology helps to provide the real time data,
and can quickly glimpse the land picture status of watershed.
This technology enforces the capability of variation region
monitoring, and could save time and cost. The result reveals
that the FUZZY and BPN have better feasibility of predictive
values, which proves that the three index is better choice in
extracting change area from image. Therefore, we will use this
experience to identify factors of NDVI, shape, and color to
establish the membership function basic module for further
research.
ACKNOWLEDGMENT

This research was supported by Soil and Water
Conservation Bureau of Council of Agriculture, under the
project “Interpretation and Investigation of the Satellite Image
Variation Point of Shihmen Dam Catchment Area” (Project
No: 970102860).
REFERENCES
[1]

approach with Formosat-2 data,” Journal of Applied Remote Sensing, vol.
3, article number. 033558, 2009.
Cheng Yeh, “Modeling Chaotic Dynamical Systems Using ExtendedNeuron Networks,” Neural, Parallel & Scientific Computations, vol. 5,
no. 4, pp. 429-438, 1999.
Teng-To Yu was born in Nantou, Taiwan. He
received the Ph.D. of Geophysics from University
of Colorado Boulder in USA in 1995. He is an
Assistant Professor at Department of Resource
Engineering, National Cheng-Kung University,
Taiwan. His current research interests are remote
sensing, GIS and LiDAR data automatic
classification.

Ting-Shiuan Wang was born in Keelung, Taiwan.
He received the M.S. in Department of Civil and
Disaster Prevention Engineering from Ching Yun
University, Taiwan, in 2007. He is a Ph.D.
Candidate in Department of Resource Engineering,
National Cheng-Kung University, Taiwan. His
current research interests are remote sensing, fuzzy
theory, and Neural Network.

IJRSA
Pei-Ling Li was born in Tainan, Taiwan. She
received the M.S. degree in Department of
Environmental Engineering from Chia Nan
University, Taiwan, in 2007. She is a Ph.D.
Candidate in Department of Resource Engineering,
National Cheng-Kung University, Taiwan. Her
current research interests are remote sensing and
GIS.

Wei-Ling Lin was born in Taipei, Taiwan. She
received the Bachelor degree in Department of
Resource Engineering from National Cheng-Kung
University, Taiwan, in 2010. She is a B.S. student
in Department of Resource Engineering, National
Cheng-Kung University, Taiwan. Her current
research interests are remote sensing and GIS.

Three Indexes Estimation in Extracting Change Area from Remote Sensing Image

http://www.ijrsa.org/ This study presents the technology to combine the remote sensing image of SPOT and FORMOSAT-2 satellite image by Fuzzy theory and Back Propagation Network (BPN). This method adopt three experience identify factors of NDVI, shape, and color to establish the membership function. The fuzzy results show that the successful rate of identification was about 87 percent. The BPN results show that the successful rate of identification was about 91 percent. Therefore the result proves that the three indexes are the best choice in extracting of change area from image, because the error is the lowest among the two methods in this study.